WO2021145228A1

WO2021145228A1 - Information processing device, and information processing method

Info

Publication number: WO2021145228A1
Application number: PCT/JP2021/000017
Authority: WO
Inventors: 悠希武田
Original assignee: ソニーグループ株式会社
Priority date: 2020-01-17
Filing date: 2021-01-04
Publication date: 2021-07-22
Also published as: JP2021114082A

Abstract

The present technology relates to an information processing device and an information processing method which enable efficient collection of data desired by a user. According to the present invention, a control unit outputs, on the basis of a learning model, candidate data serving as a candidate for assignment of the same label as input data input by a user, and outputs additional candidate data on the basis of a learning model that has learned using selected data selected as a target for assignment of a label by the user, from among the candidate data. The present technology can be applied to a computer terminal used to collect word data, for example.

Description

Information processing device and information processing method

The present technology relates to an information processing device and an information processing method, and more particularly to an information processing device and an information processing method capable of efficiently collecting data desired by a user.

The technique of guessing the relationship between vocabularies is known.

For example, Patent Document 1 discloses an inter-vocabulary relationship estimation device that defines a relationship between a plurality of vocabularies to be processed by machine learning using existing inter-vocabulary relationship data. This technique can also be applied to tasks such as collecting closely related and similar data.

Japanese Unexamined Patent Publication No. 2019-149097

However, in the technique of Patent Document 1, the relationship between the vocabularies to be processed is uniquely determined by the existing inter-vocabulary relationship data. Therefore, for example, the user cannot collect the data according to the user's request, such as setting the relationship between the data for each application.

This technology was made in view of such a situation, and enables the user to efficiently collect the desired data.

The information processing apparatus of the present technology includes a control unit that outputs candidate data that is a candidate for giving the same label as the input data input by the user based on the learning model, and the control unit is included in the candidate data. This is an information processing device that outputs further the candidate data based on the learning model learned by the user using the selection data selected as the target for assigning the label.

In the information processing method of the present technology, the information processing apparatus outputs candidate data that can be given the same label as the input data input by the user based on the learning model, and the user selects the candidate data from the candidate data. This is an information processing method for outputting further candidate data based on the learning model learned using the selection data selected as the target for assigning the label.

In the present technology, based on the learning model, candidate data that is a candidate for assigning the same label as the input data input by the user is output, and the candidate data is selected by the user as the target for assigning the label. Further candidate data is output based on the learning model trained using the selected data.

It is a figure which shows the example of a word data. It is a figure explaining the difference of word data according to a voice agent. It is a block diagram which shows the functional structure example of the information processing apparatus to which this technology is applied. It is a figure which shows the processing outline of a learning part. It is a flowchart explaining the flow of word data collection processing. It is a figure which shows the presentation example of the candidate data. It is a figure which shows the presentation example of the candidate data. It is a figure which shows the presentation example of the candidate data. It is a flowchart explaining the flow of word data collection processing. It is a figure explaining the weighting for each learning model. It is a block diagram which shows the other functional configuration example of an information processing apparatus. It is a flowchart explaining the flow of word data collection processing. It is a figure which shows the example of the translation of a word. It is a figure explaining the modification of the label assignment to word data. It is a block diagram which shows the functional configuration example of a cloud server. It is a block diagram which shows the configuration example of a computer.

Hereinafter, a mode for implementing the present technology (hereinafter referred to as an embodiment) will be described. The explanation will be given in the following order.

1. 1. Problems with conventional technology 2. First embodiment (output and presentation of candidate data)
3. 3. Second embodiment (weighting for each learning model)
4. Third embodiment (collection of multilingual word data)
5. Modification example 6. Application to cloud computing 7. Computer configuration

<1. Problems with conventional technology>
As shown in FIG. 1, the word data handled in this technology includes "words" such as "cat"-"Animal", "apple"-"Fruit", "festival"-"Event", etc. It is described by a pair of "labels" that represent the concept to which the word belongs.

When creating a voice agent (also called a smart speaker) that presents a response to a user's utterance, word data as shown in FIG. 1 is indispensable. However, collecting such word data is time consuming and costly.

Collecting word data here means giving a label of the concept to which each word belongs to a large number of words collected by some method. To automate the collection of word data, when a given word is given, an algorithm that infers the concept to which the word belongs and assigns the corresponding label is required.

However, the required word data structure may differ depending on the type of voice agent you want to create. For example, as shown in FIG. 2, when creating a voice agent 11 that supports cooking, the word "fish" should be labeled with "ingredients". On the other hand, when creating a voice agent 12 for guidance in an aquarium, the word "fish" should be labeled as "creature".

When using an existing database such as a dictionary in collecting word data, it was not possible to flexibly add labels as shown in Fig. 2. As a result, it was not possible to collect word data in response to the user's request.

Therefore, in the present technology, candidate data close to the input data input by the user is output, and further candidate data is output by learning using the selection data selected by the user from the candidate data. It enables the user to efficiently collect the desired data.

<2. First Embodiment (output and presentation of candidate data)>
(Configuration of information processing device)
FIG. 3 is a block diagram showing a configuration example of an information processing device to which the present technology is applied.

The information processing device 100 of FIG. 3 is composed of a personal computer operated by a user, a tablet terminal, a computer terminal such as a smartphone, and the like.

The information processing device 100 is composed of an input unit 110, a presentation unit 120, a storage unit 130, and a control unit 140.

The input unit 110 is composed of buttons, a keyboard, a mouse, a microphone, etc., and accepts user operations. The input information according to the user's operation is input to the control unit 140.

The presentation unit 120 is composed of an organic EL (Electro-Luminescence) display, a liquid crystal display, a speaker, and the like, and presents data and the like output by the control unit 140. The input unit 110 may be composed of a touch panel formed integrally with the display as the presentation unit 120.

The storage unit 130 is composed of a non-volatile memory such as a flash memory, and stores a program and various data read by the control unit 140.

The control unit 140 is composed of a processor such as a CPU (Central Processing Unit) and a memory. The control unit 140 includes a learning unit 141. The learning unit 141 is realized by executing a predetermined program read from the storage unit 130 by the CPU constituting the control unit 140.

(Outline of processing of the learning department)
Here, the processing outline of the learning unit 141 will be described with reference to FIG.

For example, when a user collects word data belonging to a fruit, a predetermined number of word data P1s labeled with "Fruit" are input to the learning model 141a as training data from an existing word data list. The input of the word data P1 is accepted by the input unit 110.

At this time, from the existing word data list, as word data that does not belong to fruits, word data N1 with the label "Animal", word data N2 with the label "Vegetable", and the like are word data. The same number as P1 is randomly extracted. The word data N1 and the word data N2 are also input to the learning model 141a as learning data.

The learning unit 141 learns a learning model 141a that embeds words (converts words into vectors) using an algorithm such as word2vec, using word data P1 as a positive example and word data N1 and word data N2 as negative examples. Let me.

The learning unit 141 classifies the unlabeled word data WD extracted from the vocabulary database VDB using the binary classifier 141b based on the feature amount (vector of each word) which is the learning result of the learning model 141a. The unlabeled word data WD classified on the positive example side is output to the presentation unit 120 as a candidate data CD, and is presented by the presentation unit 120. The candidate data CD is word data having a vector close to the word data P1.

In the example of FIG. 4, "kiwi", "politician", "Dorian", "tomato", ... Are classified as unlabeled word data WD, and "kiwi" and "kiwi" are classified on the regular side. "Dorian", "Tomato", ... Are output and presented as candidate data CDs.

The learning unit 141 causes the learning model 141a to input the selection data SD selected by the user from the candidate data CDs presented by the presentation unit 120 as regular learning data.

In the example of FIG. 4, "kiwi", "durian", ... Are selected as the selection data SD. At this time, the non-selected data not selected by the user from the candidate data CD is input to the learning model 141a as learning data of a negative example. In particular, in general, there is no clear definition of whether "tomato" is a fruit or a vegetable, but in this example, "tomato" is selected not to be a fruit at the request of the user.

The learning unit 141 repeats the above process until instructed by the user.

In this way, the learning unit 141 learns the input data (word data) supplied from the input unit 110 using the learning model, and outputs candidate data (unlabeled word data) close to the input data to the presentation unit 120. do. Further, the learning unit 141 learns again using the candidate data selected by the user from the candidate data presented to the presentation unit 120, and outputs new candidate data to the presentation unit 120.

(Flow of word data collection process)
Next, the flow of the word data collection process by the information processing apparatus 100 will be described with reference to the flowchart of FIG.

In step S11, the input unit 110 accepts the input of word data to which the label of the attribute that the user wants to collect is attached.

In step S12, the control unit 140 (learning unit 141) provides the presentation unit 120 with candidate data that is a candidate for giving the same label as the input word data based on the learning model trained using the input data. Output.

In step S13, the presentation unit 120 presents the candidate data output from the control unit 140.

In step S14, the control unit 140 determines whether or not the instruction to end the collection of word data has been received by the user's operation on the input unit 110.

When the input unit 110 is not instructed to end the collection of word data, but is accepted to select a predetermined candidate data as a label assignment target from the candidate data presented by the presentation unit 120. , The process proceeds to step S15.

In step S15, the control unit 140 (learning unit 141) adds the selected candidate data, which is the selected data, to the positive example, and the non-selected data, which is the non-selected candidate data, to the negative example.

In step S16, the control unit 140 (learning unit 141) trains the learning model using the selected data as a positive example and the non-selected data as a negative example. After that, the process returns to step S12, and the subsequent processes are repeated.

6 to 8 are diagrams showing an example of presentation of candidate data in the presentation unit 120.

The screens shown in FIGS. 6 to 8 are provided with a word data input area 151, a send button 152, and a candidate data display area 153-1, 153-2, 153, ...

In the word data input area 151, word data (normal learning data) to which a label of an attribute desired to be collected by the user is attached is input. In the example of FIG. 6, “cake”, “pudding”, and “cookie” labeled as “confectionery” are input as word data in the word data input area 151.

When the send button 152 is operated while the word data is input to the word data input area 151, the candidate data display area 153-1, 153-2, 153-3, ... Is filled with the candidate data 161. The

selection buttons

162P and 162N are displayed. The selection button 162P is a button for using the candidate data 161 as selection data (normal learning data). On the other hand, the selection button 162N is a button for using the candidate data 161 as non-selection data (learning data of a negative example).

In the example of FIG. 6, “chocolate”, “ramune”, and “curry” are used as candidate data 161 for assigning a “confectionery” label to each of the candidate data display areas 153-1, 153-2, and 153-3. Is displayed. The score of the binary classification is shown on the right side of each of the candidate data 161. The closer this score is to 1.0, the more the corresponding candidate data 161 is classified on the positive example side.

In the example of FIG. 7, one of the

selection buttons

162P and 162N is selected in each of the candidate data display areas 153-1, 153-2, 153-3 from the state of FIG.

Specifically, in the candidate data display area 153-1, the selection button 162P having "chocolate" of the candidate data 161 as the selection data is selected, and in the candidate data display area 1532, the "ramune" of the candidate data 161 is selected. The selection button 162P with "" as the selection data is selected. Further, in the candidate data display area 153-3, the selection button 162N having "curry" of the candidate data 161 as non-selection data is selected.

By operating the send button 152 in the state of FIG. 7, the "chocolate" and "ramune" are given the label of "confectionery".

After that, as shown in FIG. 8, the word data input area 151 is newly labeled with "confectionery" in addition to the "cake", "pudding", and "cookie" input in the state of FIG. The pudding "chocolate" and "ramune" are entered.

In this state, when the send button 152 is operated, "cheese", as further candidate data 161 for giving a label of "confectionery" to each of the candidate data display areas 153-1, 153-2, 153-3, "Almond" and "Bread" are displayed.

In this way, the selection of the presented candidate data and the presentation of the candidate data by learning using the selected data are repeated.

Now, returning to the flowchart of FIG. 5, in step S14, it is determined that the instruction to end the collection of the word data has been received, such as by determining that the user has collected a satisfactory amount of word data. The process ends. The labeled selection data, that is, the collected word data is stored in, for example, the storage unit 130.

According to the above processing, candidate data close to the word data input by the user is presented, and further candidate data is presented by learning using the selection data selected from the candidate data. As a result, it is possible to propose highly accurate word data according to the user's request, and it is possible to efficiently collect the data desired by the user.

Also, by using a high-speed algorithm in learning the learning model, the time required to output word data can be shortened. This makes it possible to improve the accuracy of word data proposals by interactive learning without stressing the user.

<3. Second embodiment (weighting for each learning model)>
Depending on the type of voice agent you want to create, the context in which word data should be collected may differ. For example, when creating a voice agent that introduces news, you want to handle many hard-expressing words used in news, but when creating a voice agent that is a friendly conversation partner, you want to use buzzwords and broken expressions. I want to handle many words.

Therefore, we will explain an example of preparing multiple learning models in advance and presenting candidate data for each learning model, such as a word2vec model learned from the contents of newspaper articles and a word2vec model learned from the contents of SNS (Social Networking Service) posts. do.

(Flow of word data collection process)
The flow of the word data collection process using the plurality of learning models will be described with reference to the flowchart of FIG.

Note that the processing of steps S21, S23 to S26 in the flowchart of FIG. 9 is basically the same as the processing of steps S11, S13 to S16 in the flowchart of FIG. 5, so the description thereof will be omitted.

That is, in step S22, the control unit 140 (learning unit 141) provides candidate data as a candidate to be given the same label as the input word data for each learning model based on a plurality of learning models prepared in advance. , Is output to the presentation unit 120. At this time, the same number of candidate data is output for each learning model.

After that, a predetermined candidate data is selected from the candidate data presented by the presentation unit 120. Then, in step S27, the control unit 140 (learning unit 141) changes the weighting of the number of candidate data output for each learning model according to the selected candidate data (selected data). After that, the process returns to step S22, and the subsequent processes are repeated.

FIG. 10 is a diagram illustrating weighting of the number of candidate data for each learning model.

In the example of FIG. 10, a word2vec model 210 learned from the contents of a newspaper article and a word2vec model 220 learned from the contents of an SNS post are prepared.

First, as shown in FIG. 10A, the same number (for example, 5) of candidate data 231 is output from each of the

word2vec models

210 and 220.

In the example of FIG. A, four of the five candidate data 231 output from the word2vec model 210 are selected as selection data, and one of the five candidate data 231 output from the word2vec model 220 is selected as selection data. Has been done.

In this case, it is presumed that the user wants to collect word data of rigid expressions used in newspaper articles.

As a result, the weighting of the number of candidate data output from each of the

word2vec models

210 and 220 is changed. Specifically, for the word2vec model in which more selection data is output, the weighting is changed so that more candidate data is output.

For example, as shown in FIG. 10B, seven candidate data 232 are output from the word2vec model 210, and three candidate data 232 are output from the word2vec model 220.

According to the above processing, the weighting of the number of candidate data output for each learning model is changed according to the selection data selected by the user, so that the word data in the context desired by the user can be collected. Is possible.

<4. Third embodiment (collection of multilingual word data)>
When developing a voice agent that supports multiple languages, it is necessary to prepare as many word data as there are languages that support it. This is a burden for voice agent developers.

Therefore, an example will be described in which word data of one language (for example, Japanese) is collected by the above-mentioned function, and then these word data are automatically translated into each language.

(Configuration of information processing device)
FIG. 11 is a block diagram showing a configuration example of the information processing apparatus of the present embodiment.

The information processing device 100 of FIG. 11 is different from the information processing device 100 of FIG. 3 in that the control unit 140 includes a translation processing unit 311 in addition to the learning unit 141.

The translation processing unit 311 translates the input data (word data) input by the user and the candidate data (selected data) to which the same label as the input data is given into a predetermined language. Language translation is performed, for example, by using a dictionary of the corresponding language stored in the storage unit 130.

(Flow of word data collection process)
Next, the flow of the word data collection process for translating the collected word data into a predetermined language will be described with reference to the flowchart of FIG.

Note that the processing of steps S31 to S36 in the flowchart of FIG. 12 is basically the same as the processing of steps S11 to S16 in the flowchart of FIG. 5, so the description thereof will be omitted.

That is, after it is determined in step S34 that the instruction to end the collection of word data has been received, in step S37, the translation processing unit 311 selects the regular word data (that is, the input data and the selection data). , Translate into the given language.

For example, as shown in FIG. 13, when word data 330 belonging to a fruit is collected as regular word data, the word data 330 includes Chinese word data 330C, English word data 330E, and Russian. Is translated into the word data 330R of.

According to the above processing, the man-hours for collecting multilingual word data can be shortened, and the cost and time required for developing a voice agent corresponding to multiple languages can be reduced.

<5. Modification example>
Hereinafter, a modified example of the above-described embodiment will be described.

(Example of presentation)
In the above-described embodiment, the user selects the word data on the screen as shown in FIGS. 6 to 8 to give the word data a label.

Not limited to this, word data may be selected on the screen 350 for providing the quiz game as shown in FIG. 14, and a label may be given to the word data.

At the upper part of the screen 350 of FIG. 14, a question sentence for selecting a word of an attribute that the user wants to collect, specifically, a word belonging to a fruit is displayed. Below the question, buttons 361 to 364 for selecting "Dorian", "Tomato", "Table", and "Chair", which are candidates for the word of the attribute (fruit), are displayed.

The user selects the button of the word that is considered to be a fruit so that the user answers the question "Which fruit?", And the selected word is given a fruit label.

By providing such a quiz game to more players, it is possible to efficiently collect the data desired by the user.

(Example of input data)
In the above-described embodiment, word data is input as input data, but data in other formats may be input.

Specifically, image data may be input as input data. In this case, as candidate data, candidate image data to be given the same label as the input image data is presented, and further image data is presented by learning using the image data selected from the candidate data.

As a result, image data close to the input image data can be efficiently collected, and for example, the accuracy of similar image search can be improved.

Also, as input data, sensor data obtained from various sensors can be input.

For example, the acceleration data obtained from the acceleration sensor or the angular acceleration data obtained from the gyro sensor may be input as the input data. In this case, as candidate data, sensor data that gives the same label as the input sensor data is presented as acceleration or rotation in, for example, VR (Virtual Reality) goggles. Further sensor data is presented by learning using the sensor data selected from them.

This makes it possible to efficiently collect sensor data that realizes acceleration and rotation close to the input sensor data, and for example, it is possible to enhance the immersive feeling in the VR experience.

<6. Application to cloud computing>
This technology can also be applied to cloud computing.

FIG. 15 is a block diagram showing a functional configuration example of an information processing system in which this technology is applied to cloud computing.

The information processing system of FIG. 15 is composed of a terminal device 400 and a cloud server 500. The terminal device 400 and the cloud server 500 are connected by a network NW such as the Internet, and can communicate with each other.

The terminal device 400 is composed of a computer terminal such as a PC, a tablet terminal, or a smartphone operated by a user, and includes at least an input unit 410 and a presentation unit 420. The input unit 410 and the presentation unit 420 correspond to the input unit 110 and the presentation unit 120 of FIG. 3, respectively.

On the other hand, the cloud server 500 is composed of a large computer device, and includes at least a storage unit 510 and a control unit 520. The control unit 520 includes a learning unit 521. The storage unit 510, the control unit 520, and the learning unit 521 correspond to the storage unit 130, the control unit 140, and the learning unit 141 of FIG. 3, respectively. The storage unit 510 may be provided in a database server or the like configured separately from the cloud server 500.

In the information processing system of FIG. 15, the terminal device 400 transmits the input data to the cloud server 500.

Based on the learning model, the cloud server 500 transmits candidate data that is a candidate to be given the same label as the input data from the terminal device 400 to the terminal device 400.

The terminal device 400 presents candidate data, and one of them is selected by the user as a label addition target. The selected candidate data (selected data) is transmitted to the cloud server 500.

The cloud server 500 transmits further candidate data to the terminal device 400 based on the learning model learned using the candidate data (selected data) selected in the terminal device 400.

Even in such a configuration, it is possible to propose highly accurate word data according to the user's request, and it is possible to efficiently collect the data desired by the user.

<7. Computer configuration>
The series of processes described above can be executed by hardware or software. When a series of processes are executed by software, the programs constituting the software are installed from the program recording medium on a computer embedded in dedicated hardware, a general-purpose personal computer, or the like.

FIG. 16 is a block diagram showing a configuration example of computer hardware that executes the above-mentioned series of processes programmatically.

The information processing device 100 and the cloud server 500 described above are realized by the computer 1000 having the configuration shown in FIG.

The CPU 1001, ROM 1002, and RAM 1003 are connected to each other by the bus 1004.

An input / output interface 1005 is further connected to the bus 1004. An input unit 1006 including a keyboard and a mouse, and an output unit 1007 including a display and a speaker are connected to the input / output interface 1005. Further, the input / output interface 1005 is connected to a storage unit 1008 including a hard disk and a non-volatile memory, a communication unit 1009 including a network interface, and a drive 1010 for driving the removable media 1011.

In the computer 1000 configured as described above, the CPU 1001 loads the program stored in the storage unit 1008 into the RAM 1003 via the input / output interface 1005 and the bus 1004 and executes the program, thereby executing the series described above. Processing is done.

The program executed by the CPU 1001 is recorded on the removable media 1011 or provided via a wired or wireless transmission medium such as a local area network, the Internet, or digital broadcasting, and is installed in the storage unit 1008.

The program executed by the computer 1000 may be a program in which processing is performed in chronological order in the order described in this specification, or at a required timing such as in parallel or when a call is made. It may be a program that is processed by.

The embodiment of the present technology is not limited to the above-described embodiment, and various changes can be made without departing from the gist of the present technology.

Further, the effects described in the present specification are merely examples and are not limited, and other effects may be obtained.

Further, the present technology can have the following configuration.
(1)
Based on the learning model, it is equipped with a control unit that outputs candidate data that is a candidate to be given the same label as the input data input by the user.
The control unit is an information processing device that outputs further candidate data based on the learning model learned by using the selected data selected by the user as the target for assigning the label from the candidate data.
(2)
The control unit trains the learning model using the selected data as a positive example and non-selected data not selected by the user as a target for assigning the label from the candidate data as a negative example (1). The information processing device described.
(3)
The information processing device according to (1) or (2), wherein the control unit outputs the candidate data for each of the learning models based on the plurality of learning models.
(4)
The control unit
The same number of candidate data is output for each learning model, and the same number of candidate data is output.
The information processing apparatus according to (3), wherein the weighting of the number of candidate data output for each learning model is changed according to the selected data selected by the user.
(5)
The information processing device according to (4), wherein the control unit changes the weighting of the learning model to which more selection data is output so that more candidate data is output.
(6)
The information processing device according to any one of (1) to (5), wherein the input data is word data.
(7)
The information processing device according to (6), wherein the learning model is a word2vec model.
(8)
The information processing apparatus according to (6) or (7), further comprising a translation processing unit that translates the input data and the selected data to which the label is attached into a predetermined language.
(9)
The information processing device according to (1), wherein the input data is image data.
(10)
The information processing device according to (1), wherein the input data is sensor data.
(11)
The information processing device according to (10), wherein the sensor data is acceleration data.
(12)
The information processing device according to (10), wherein the sensor data is angular acceleration data.
(13)
A presentation unit that presents the output candidate data to the user,
The information processing apparatus according to any one of (1) to (12), further comprising an input unit that accepts selection of the selected data from the presented candidate data.
(14)
Information processing device
Based on the learning model, output candidate data that can be given the same label as the input data input by the user, and output the candidate data.
An information processing method for outputting further candidate data based on the learning model learned by using the selected data selected by the user as the target for assigning the label from the candidate data.

100 information processing device, 110 input unit, 120 presentation unit, 130 storage unit, 140 control unit, 141 learning unit, 311 translation processing unit, 500 cloud server

Claims

Based on the learning model, it is equipped with a control unit that outputs candidate data that is a candidate to be given the same label as the input data input by the user.
The control unit is an information processing device that outputs further candidate data based on the learning model learned by using the selected data selected by the user as the target for assigning the label from the candidate data.
According to claim 1, the control unit trains the learning model using the selected data as a positive example and non-selected data not selected by the user as a target for assigning the label from the candidate data as a negative example. The information processing device described.
The information processing device according to claim 1, wherein the control unit outputs the candidate data for each of the learning models based on the plurality of learning models.
The control unit
The same number of candidate data is output for each learning model, and the same number of candidate data is output.
The information processing apparatus according to claim 3, wherein the weighting of the number of candidate data output for each learning model is changed according to the selected data selected by the user.
The information processing device according to claim 4, wherein the control unit changes the weighting of the learning model to which more selection data is output so that more candidate data is output.
The information processing device according to claim 1, wherein the input data is word data.
The information processing device according to claim 6, wherein the learning model is a word2vec model.
The information processing apparatus according to claim 6, further comprising a translation processing unit that translates the input data and the selected data to which the label is attached into a predetermined language.
The information processing device according to claim 1, wherein the input data is image data.
The information processing device according to claim 1, wherein the input data is sensor data.
The information processing device according to claim 10, wherein the sensor data is acceleration data.
The information processing device according to claim 10, wherein the sensor data is angular acceleration data.
A presentation unit that presents the output candidate data to the user,
The information processing apparatus according to claim 1, further comprising an input unit that accepts selection of the selected data from the presented candidate data.
Information processing device
Based on the learning model, output candidate data that can be given the same label as the input data input by the user, and output the candidate data.
An information processing method for outputting further candidate data based on the learning model learned by using the selected data selected by the user as the target for assigning the label from the candidate data.